Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- RSA
- RPV
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 6W. RSA
- 1Y. RSA
- 6W. RPV
- 1Y. RPV
- 6W. RLL
- 1Y. RLL
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- Ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- RSA
- RPV
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt

Propensity Scores Common Support

Model Stats

  • Treatment proportion: 0.118
  • Model Type: elastic_net
  • Accuracy: 0.9025641
  • Params: alpha: 0.1 lambda: 0.0032359

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
      0%      25%      50%      75%     100% 
-72.0000 -21.1525 -10.9750  -4.0000  27.5500 
Model Type Y: boosting 
RMSE: 22.650482584454 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 13.6548765550606 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 2.307 (Std.Error: 4.316)
Trimmed ATE (Yes-No): 2.696 (Std.Error: 4.506)
Upper ATE (Yes-No): -6.757 (Std.Error: 5.755)
Observational differences in treatment 2.634 (Yes-No) 

   treatment  outcome
1:       Yes 23.61944
2:        No 20.98496
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Major curve Cobb angle
Distribution:
     0%     25%     50%     75%    100% 
-64.000 -22.785 -10.000  -3.000  22.440 
Model Type Y: boosting 
RMSE: 21.5458662067798 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 14.3992415137597 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -1.306 (Std.Error: 9.419)
Trimmed ATE (Yes-No): -0.817 (Std.Error: 9.762)
Upper ATE (Yes-No): -13.227 (Std.Error: 9.06)
Observational differences in treatment 3.876 (Yes-No) 

   treatment  outcome
1:       Yes 24.72233
2:        No 20.84643
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-22.181906  -6.000000  -1.505162   1.596953  18.000000 
Model Type Y: boosting 
RMSE: 6.5415287611348 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 5.8851768848087 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -4.729 (Std.Error: 1.785)
Trimmed ATE (Yes-No): -4.955 (Std.Error: 1.849)
Upper ATE (Yes-No): 0.706 (Std.Error: 3.371)
Observational differences in treatment -1.965 (Yes-No) 

   treatment   outcome
1:       Yes -4.603244
2:        No -2.638455
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-22.000000  -6.000000  -2.018531   1.038160  20.000000 
Model Type Y: boosting 
RMSE: 7.00310806375334 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 5.90951275955555 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -3.564 (Std.Error: 2.057)
Trimmed ATE (Yes-No): -3.75 (Std.Error: 2.124)
Upper ATE (Yes-No): 0.518 (Std.Error: 3.025)
Observational differences in treatment -1.491 (Yes-No) 

   treatment   outcome
1:       Yes -4.099762
2:        No -2.609148
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-192.0000  -69.0075  -30.0050   -0.7175   89.0000 
Model Type Y: boosting 
RMSE: 51.5280795984595 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 52.4452131341047 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -33.923 (Std.Error: 14.214)
Trimmed ATE (Yes-No): -35.634 (Std.Error: 14.691)
Upper ATE (Yes-No): 0.15 (Std.Error: 25.031)
Observational differences in treatment -17.122 (Yes-No) 

   treatment  outcome
1:       Yes 16.58457
2:        No 33.70692
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-192.5100  -67.2100  -30.3250    6.0475   89.3700 
Model Type Y: boosting 
RMSE: 55.5816899164131 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 52.75061113174 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -32.422 (Std.Error: 16.469)
Trimmed ATE (Yes-No): -33.249 (Std.Error: 16.998)
Upper ATE (Yes-No): -13.399 (Std.Error: 25.743)
Observational differences in treatment -18.922 (Yes-No) 

   treatment  outcome
1:       Yes 18.76893
2:        No 37.69094
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-57.000 -18.115  -6.090   1.880 149.410 
Model Type Y: boosting 
RMSE: 14.6165869618543 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 14.7472349841479 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -8.144 (Std.Error: 3.669)
Trimmed ATE (Yes-No): -8.373 (Std.Error: 3.83)
Upper ATE (Yes-No): -3.048 (Std.Error: 5.567)
Observational differences in treatment -6.928 (Yes-No) 

   treatment  outcome
1:       Yes 18.07389
2:        No 25.00188
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-37.060 -16.205  -5.790   1.000  26.000 
Model Type Y: boosting 
RMSE: 15.4688745829409 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 11.4274573983862 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -11.483 (Std.Error: 4.159)
Trimmed ATE (Yes-No): -11.762 (Std.Error: 4.265)
Upper ATE (Yes-No): -5.306 (Std.Error: 8.212)
Observational differences in treatment -5.792 (Yes-No) 

   treatment  outcome
1:       Yes 20.13759
2:        No 25.92920
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-71.00 -24.00  -9.85   0.00  29.00 
Model Type Y: boosting 
RMSE: 18.5847775183469 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 15.617838335267 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -2.637 (Std.Error: 6.253)
Trimmed ATE (Yes-No): -2.384 (Std.Error: 6.523)
Upper ATE (Yes-No): -8.56 (Std.Error: 6.633)
Observational differences in treatment -1.752 (Yes-No) 

   treatment  outcome
1:       Yes -51.3150
2:        No -49.5634
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-67.87 -25.00  -8.01   0.00  23.38 
Model Type Y: boosting 
RMSE: 22.3146183457342 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 15.6553644773089 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -10.418 (Std.Error: 6.526)
Trimmed ATE (Yes-No): -10.489 (Std.Error: 6.714)
Upper ATE (Yes-No): -8.677 (Std.Error: 9.177)
Observational differences in treatment 1.84 (Yes-No) 

   treatment   outcome
1:       Yes -47.61793
2:        No -49.45805
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. LGap
Distribution:
      0%      25%      50%      75%     100% 
-71.0000 -24.5400  -9.3722   0.5004  78.9200 
Model Type Y: boosting 
RMSE: 19.6420666844899 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 17.3428352905024 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -3.407 (Std.Error: 6.962)
Trimmed ATE (Yes-No): -3.328 (Std.Error: 7.305)
Upper ATE (Yes-No): -5.254 (Std.Error: 6.681)
Observational differences in treatment -3.566 (Yes-No) 

   treatment  outcome
1:       Yes 10.18330
2:        No 13.74943
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. LGap
Distribution:
      0%      25%      50%      75%     100% 
-67.7242 -24.9922  -8.0484   0.2242  22.0800 
Model Type Y: boosting 
RMSE: 22.6850437069752 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 15.8744008290482 
Params: nrounds: 150.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -11.644 (Std.Error: 7.754)
Trimmed ATE (Yes-No): -11.608 (Std.Error: 8.043)
Upper ATE (Yes-No): -12.528 (Std.Error: 8.593)
Observational differences in treatment -1.175 (Yes-No) 

   treatment  outcome
1:       Yes 12.44743
2:        No 13.62279
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Pelvic Tilt
Distribution:
      0%      25%      50%      75%     100% 
-31.0000  -8.2775  -2.0750   2.1575  14.4200 
Model Type Y: boosting 
RMSE: 10.8059387816646 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 7.38244936984984 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -3.459 (Std.Error: 3.14)
Trimmed ATE (Yes-No): -3.489 (Std.Error: 3.207)
Upper ATE (Yes-No): -2.699 (Std.Error: 4.724)
Observational differences in treatment -3.942 (Yes-No) 

   treatment  outcome
1:       Yes 18.14114
2:        No 22.08345
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
     0%     25%     50%     75%    100% 
-26.620  -7.000  -2.005   2.000  23.000 
Model Type Y: boosting 
RMSE: 9.96806582045828 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.66554574216582 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -7.238 (Std.Error: 3.554)
Trimmed ATE (Yes-No): -7.497 (Std.Error: 3.708)
Upper ATE (Yes-No): -0.992 (Std.Error: 3.577)
Observational differences in treatment -3.605 (Yes-No) 

   treatment  outcome
1:       Yes 19.15069
2:        No 22.75583
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RSA
Distribution:
      0%      25%      50%      75%     100% 
-57.0000 -18.0867  -6.2346   2.1227  76.5028 
Model Type Y: boosting 
RMSE: 15.5776727847706 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 12.8880388700647 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -7.675 (Std.Error: 4.096)
Trimmed ATE (Yes-No): -7.897 (Std.Error: 4.278)
Upper ATE (Yes-No): -2.75 (Std.Error: 5.386)
Observational differences in treatment -5.371 (Yes-No) 

   treatment   outcome
1:       Yes  7.350956
2:        No 12.721711
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RSA
Distribution:
      0%      25%      50%      75%     100% 
-37.0000 -16.5150  -5.7076   1.0000  25.0400 
Model Type Y: boosting 
RMSE: 15.1126613560699 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 11.5843558928371 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -11.408 (Std.Error: 4.324)
Trimmed ATE (Yes-No): -11.785 (Std.Error: 4.54)
Upper ATE (Yes-No): -3.046 (Std.Error: 6.414)
Observational differences in treatment -2.838 (Yes-No) 

   treatment  outcome
1:       Yes 10.68837
2:        No 13.52673
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RPV
Distribution:
        0%        25%        50%        75%       100% 
-85.555100  -2.242325   2.157300   8.204075  31.000000 
Model Type Y: boosting 
RMSE: 10.0806392686394 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 8.59395331544651 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): 4.137 (Std.Error: 2.6)
Trimmed ATE (Yes-No): 4.245 (Std.Error: 2.736)
Upper ATE (Yes-No): 1.599 (Std.Error: 4.009)
Observational differences in treatment 3.603 (Yes-No) 

   treatment   outcome
1:       Yes -4.665828
2:        No -8.269321
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RPV
Distribution:
      0%      25%      50%      75%     100% 
-22.1800  -1.4100   2.3588   6.4616  26.6346 
Model Type Y: boosting 
RMSE: 10.3685381160092 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.50421319927846 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): 6.464 (Std.Error: 2.521)
Trimmed ATE (Yes-No): 6.693 (Std.Error: 2.561)
Upper ATE (Yes-No): 0.923 (Std.Error: 4.211)
Observational differences in treatment 1.186 (Yes-No) 

   treatment   outcome
1:       Yes -7.267676
2:        No -8.453427
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RLL
Distribution:
       0%       25%       50%       75%      100% 
-87.18180  -0.33685   9.36330  24.71500  71.00000 
Model Type Y: boosting 
RMSE: 19.1964426265129 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 16.8619173828334 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): 3.168 (Std.Error: 6.609)
Trimmed ATE (Yes-No): 3.006 (Std.Error: 6.922)
Upper ATE (Yes-No): 6.955 (Std.Error: 7.878)
Observational differences in treatment 3.62 (Yes-No) 

   treatment   outcome
1:       Yes -10.91046
2:        No -14.53047
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RLL
Distribution:
       0%       25%       50%       75%      100% 
-22.58000  -0.37805   8.02520  25.03075  67.70260 
Model Type Y: boosting 
RMSE: 24.1687258441796 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 15.9551845299249 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): 11.547 (Std.Error: 8.769)
Trimmed ATE (Yes-No): 11.534 (Std.Error: 9.192)
Upper ATE (Yes-No): 11.856 (Std.Error: 9.916)
Observational differences in treatment 1.639 (Yes-No) 

   treatment   outcome
1:       Yes -12.96230
2:        No -14.60109
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'